Constrained Optimization with Evolutionary Algorithms: A Comprehensive Review

Global optimization is an essential part of any kind of system. Various algorithms have been proposed that try to imitate the learning and problem solving abilities of the nature up to certain level. The main idea of all nature-inspired algorithms is to generate an interconnected network of individuals, a population. Although most of unconstrained optimization problems can be easily handled with Evolutionary Algorithms (EA), constrained optimization problems (COPs) are very complex. In this paper, a comprehensive literature review will be presented which summarizes the constraint handling techniques for COP

Meta-heuristic based Construction Supply Chain Modelling and Optimization

Driven by the severe competition within the construction industry, the necessity of improving and optimizing the performance of construction supply chain has been aroused. This thesis proposes three problems with regard to the construction supply chain optimization from three perspectives, namely, deterministic single objective optimization, stochastic optimization and multi-objective optimization respectively. Mathematical models for each problem are constructed accordingly and meta-heuristic algorithms are developed and applied for resolving these three problems

A hybrid HS-CSS algorithm for simultaneous analysis, design and optimization of trusses via force method

In this paper, a hybrid heuristic method is developed using the harmony search (HS) and charged system search (CSS), called HS-CSS. In this algorithm the use of HS improves the exploitation property of the standard CSS. An energy formulation of the force method is developed and the analysis, design and optimization are performed simultaneously using the standard CSS and HS-CSS. New goal functions are introduced for minimization, and the CSS and the HS-CSS are employed for continuous optimization. An efficient method is introduced using the CSS and HS-CSS for designing structures having members of prescribed stress ratios. Finally, the minimum weight design of truss structures is formulated using the CSS and HS-CSS algorithms and applied to some benchmark problems from literature

Population-based algorithms for improved history matching and uncertainty quantification of Petroleum reservoirs

In modern field management practices, there are two important steps that shed light on a multimillion dollar investment. The first step is history matching where the simulation model is calibrated to reproduce the historical observations from the field. In this inverse problem, different geological and petrophysical properties may provide equally good history matches. Such diverse models are likely to show different production behaviors in future. This ties the history matching with the second step, uncertainty quantification of predictions. Multiple history matched models are essential for a realistic uncertainty estimate of the future field behavior. These two steps facilitate decision making and have a direct impact on technical and financial performance of oil and gas companies. Population-based optimization algorithms have been recently enjoyed growing popularity for solving engineering problems. Population-based systems work with a group of individuals that cooperate and communicate to accomplish a task that is normally beyond the capabilities of each individual. These individuals are deployed with the aim to solve the problem with maximum efficiency. This thesis introduces the application of two novel population-based algorithms for history matching and uncertainty quantification of petroleum reservoir models. Ant colony optimization and differential evolution algorithms are used to search the space of parameters to find multiple history matched models and, using a Bayesian framework, the posterior probability of the models are evaluated for prediction of reservoir performance. It is demonstrated that by bringing latest developments in computer science such as ant colony, differential evolution and multiobjective optimization, we can improve the history matching and uncertainty quantification frameworks. This thesis provides insights into performance of these algorithms in history matching and prediction and develops an understanding of their tuning parameters. The research also brings a comparative study of these methods with a benchmark technique called Neighbourhood Algorithms. This comparison reveals the superiority of the proposed methodologies in various areas such as computational efficiency and match quality

Solution of the Multi-objective Economic and Emission Load Dispatch Problem Using Adaptive Real Quantum Inspired Evolutionary Algorithm

Economic load dispatch is a complex and significant problem in power generation. The inclusion of emission with economic operation makes it a Multi-objective economic emission load dispatch (MOEELD) problem. So it is a tough task to resolve a constrained MOEELD problem with antagonistic multiple objectives of emission and cost. Evolutionary Algorithms (EA) have been widely used for solving such complex multi-objective problems. However, the performance of EAs on such problems is dependent on the choice of the operators and their parameters, which becomes a complex issue to solve in itself. The present work is carried out to solve a Multi-objective economic emission load dispatch problem using a Multi-objective adaptive real coded quantum-inspired evolutionary algorithm (MO-ARQIEA) with gratifying all the constraints of unit and system. A repair-based constraint handling and adaptive quantum crossover operator (ACO) are used to satisfy the constraints and preserve the diversity of the suggested approach. The suggested approach is evaluated on the IEEE 30-Bus system consisting of six generating units. These results obtained for different test cases are compared with other reputed and well-known techniques

Time-cost-quality trade-off analysis for construction projects

The main objective of construction projects is to finish the project according to an available budget, within a planned schedule, and achieving a pre-specified extent of quality. Therefore, time, cost, and quality are considered the most important attributes of construction projects. The purpose of this study is to incorporate quality into the traditional two-dimensional time-cost trade-off (TCT) in order to develop an advanced three-dimensional time-cost-quality trade-off (TCQT) approach. Time, cost, and quality of construction projects are interrelated and have impacts on each other. It is a challenging task to strike a balance among these three conflicting objectives of construction projects since no one solution can be optimal for the three objectives. The overall performance of a project regarding time, cost, and quality is determined by the duration, cost, and quality of its activities. These attributes of each activity depend on the execution option by which the activity’s work is completed. It is required to develop an approach that is capable of finding an optimal or near optimal set of execution options for the project’s activities in order to minimize the project’s total cost and total duration, while its overall quality is maximized. For the aforementioned purpose, three various Microsoft Excel based TCQT models have been developed as follows: • First, a simplified model is developed with the objective of optimizing the total duration, cost, and quality of simple construction projects utilizing the GA-based Excel add in Evolver. • Second, a stochastic model is developed with the objective of optimizing the total duration, cost, and quality of construction projects applying the PERT approach in order to consider uncertainty associated with the performance of execution options and the whole project. • Third, an advanced multi objective optimization model is developed utilizing a self-developed optimization tool having the following capabilities: 1. Selecting an appropriate execution option for each activity within a considered project to optimize the objectives of time, cost, and quality. 2. Considering the discrete nature of duration, cost, and quality of various options for executing each activity. 3. Applying three various optimization approaches, which are the Goal Programming (GP), the Modified Adaptive Weight Approach (MAWA), and the Non-dominated Sorting Genetic Algorithms (NSGAII). 4. Analyzing both TCT and TCQT problems. 5. Considering finish-to-finish, start-to-start, and start-to-finish dependency relationships in addition to the traditional finish-to-start relationships among activities. 6. Considering any number of successors and predecessors for activities. 7. User-friendly input and output interfaces to be used for large-scale projects. To validate the developed models and demonstrate their efficiency, they were applied to case studies introduced in literature. Results obtained by the developed models demonstrated their effectiveness and efficiency in analyzing both TCT and TCQT problems